An incremental self-excitation method for effectively identifying low-frequency frequency response function of milling robots

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Journal of Manufacturing Science and Engineering-transactions of The Asme Pub Date : 2023-08-10 DOI:10.1115/1.4063155
Jiawei Wu, X. Tang, Shihao Xin, Chenyang Wang, F. Peng, R. Yan, Xinyong Mao
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Abstract

Robotic machining efficiency and accuracy are limited by milling vibration and chatter. Robot dynamic characteristics are strongly dependent on the poses; therefore, acquiring the robot dynamic characteristics in any pose is important for vibration suppression and chatter avoidance in large-range machining. This paper proposes an incremental self-excitation method for effectively identifying low-frequency frequency response functions (FRF) of milling robots. A fully knowable and controllable excitation increment can be achieved by attaching a mass block at the robot end, which overcomes the shortcoming of the traditional self-excitation methods that cannot obtain the dynamic compliance magnitude. With appropriate trajectory programming, this method can be carried out automatically in the poses of interest without manual operations. First, the impulse (moment) of the incremental self-excitation is modeled based on momentum theorem, and the association model of the pulse response increment with the incremental self-excitation is established. For the problem that the FRF calculation process is sensitive to noise, the incremental self-excitation is assumed to be a Gaussian pulse, and its identification method is provided. Then, the dimensionality requirement for identifying the 9-item (direct and cross) FRFs is reduced using the modal directionality of milling robots, and the corresponding FRF calculation method is proposed. The rationality of the required simplifications and assumptions of this method is verified by experiments and calculations. The experimental results in several robot poses show that the proposed method can effectively identify all the direct and cross FRFs in the low-frequency band.
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一种有效识别铣削机器人低频频响函数的增量自激励方法
铣削振动和颤振限制了机器人的加工效率和精度。机器人的动态特性强烈依赖于姿态;因此,获取机器人在任意姿态下的动态特性,对于大范围加工中的振动抑制和颤振避免具有重要意义。针对铣削机器人低频频响函数的识别问题,提出了一种增量自激励方法。通过在机器人末端附加质量块,可以获得完全可知的可控激励增量,克服了传统自激励方法无法获得动态柔度大小的缺点。通过适当的轨迹规划,该方法可以在不需要人工操作的情况下自动完成感兴趣的姿态。首先,基于动量定理对增量自激的脉冲(力矩)进行建模,建立脉冲响应增量与增量自激的关联模型;针对频响计算过程对噪声敏感的问题,将增量自激假设为高斯脉冲,并给出了其识别方法。然后,利用铣削机器人的模态方向性降低识别9项(直接和交叉)频响的维数要求,并提出相应的频响计算方法;通过实验和计算验证了该方法所要求的简化和假设的合理性。在多个机器人姿态下的实验结果表明,该方法能有效地识别出低频段的所有直接频响和交叉频响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
期刊介绍: Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining
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